Robot and control method

ABSTRACT

With a robot and a control method for it, information is read in from the outside, based on which a particular object is detected, and it is judged upon detecting the object whether or not the object satisfies given conditions, based on the judgment results of which a robot generates predetermined actions. Thus, a robot can be realized that acts naturally like a living thing. Consequently, its entertaining quality for human beings can be increased greatly.

TECHNICAL FIELD

[0001] The present invention relates to a robot and a control method forit, and is suitably applied to a pet robot.

BACKGROUND OF THE INVENTION

[0002] Of late, a four legged pet robot, which acts in accordance todirections from a user and surroundings, has been introduced anddeveloped by this patent applicant. It is in the form of a dog or catkept by an ordinary family and so designed as to take a lie-down postureupon receiving the ‘LIE-DOWN’ directions from the user, or to profferits hand when the user extends his hand toward it.

[0003] However, if such a pet robot could behave more naturally like aliving thing, the user may get a bigger sense of affinity andsatisfaction. Therefore the entertaining quality of the pet robot willbe substantially enhanced.

DISCLOSURE OF THE INVENTION

[0004] The present invention has been done considering this point and isintended to introduce a robot and a control method for it, which canincrease the entertaining nature substantially.

[0005] In order to achieve this objective, a robot embodying the presentinvention comprises an external information read-in means for read ininformation from the outside, a detecting means for detecting aparticular object based on the read-in information, a judging means forjudging whether or not a detected object satisfies given conditions whenit has been detected by the detecting means, and an action generationmeans for generating a corresponding action based on the judgment resultobtained by the judging means. As a result, with this robot, morenatural actions as a living thing does can be achieved.

[0006] Furthermore, a robot embodying the present invention is providedwith three steps; 1) the step wherein external information is read in,based on which a particular object is detected, 2) another step whereinthe detected object is judged whether or not it satisfies givenconditions when it has been detected, and 3) yet another step whereinthe robot generates a given action based on the judgment results. As aresult, with the control method of this robot, more natural actions as aliving thing does can be achieved by way of a robot.

BRIEF DESCRIPTION OF DRAWINGS

[0007]FIG. 1 is a perspective view showing the external configuration ofa robot for entertainment, embodying the present invention.

[0008]FIG. 2 is a block diagram showing the circuit configuration of arobot for entertainment, embodying the present invention.

[0009]FIG. 3 is a block diagram for use in explaining the processes ofthe controller regarding action generation.

[0010]FIG. 4 is a schematic diagram of the probability automaton.

[0011]FIG. 5 is a conceptual diagram of the condition transition.

[0012]FIG. 6 is a schematic diagram of the emotion tables.

[0013]FIG. 7 is a flowchart showing the emotion table making andalteration process procedure.

[0014]FIG. 8 is a flowchart showing the action control processprocedure.

[0015]FIG. 9 is a flowchart showing the action determining processprocedure.

[0016]FIG. 10 is a conceptual diagram for use in describing thecondition transition.

[0017]FIG. 11 is a block diagram showing the configurations of the facepattern learning and the recognition processor.

BEST MODE FOR CARRYING OUT THE INVENTION

[0018] A mode of carrying out the present invention is described indetail, referring to the drawings

[0019] (1) The configuration of a pet robot embodying the presentinvention.

[0020] In FIG. 1, 1 is a pet robot in whole in the present embodiment,with a body unit 2, to which each of leg units 3A˜3D is attached at theleft and right side of the front and rear part as well as a head unit 4and a tail unit 5 at the front and rear end of the body unit 2respectively.

[0021] In this instance, as shown in FIG. 2, the body unit 2 comprises acontroller 10 for controlling the whole operation of the pet robot 1, abattery 11 or power source for the pet robot 1, an internal sensorcomponent 14 consisting of a battery sensor 12 and a thermal sensor 13.

[0022] Also, the head unit 4 comprises a microphone 15 working as the‘ear’ of the pet robot 1, a CCD (Charge Coupled Device) camera 16functioning as the ‘eye’, and an external sensor 19 consisting of atouch sensor 17 and a distance sensor 18, which is placed on the upperpart of the head unit 4 as shown in FIG. 1, and a speaker 20 working asthe ‘mouth’, all arranged in place.

[0023] Furthermore, an actuator 21A˜21N is placed respectively at thejoint of each leg unit 3A˜3D, at the linkage point of each leg unit3A˜3D and the body unit 2, at the linkage point of the head unit 4 andthe body unit 2, as well as at the linkage point of the tail unit 5 andthe body unit 2.

[0024] The microphone 15 of the external sensor collects external soundsand feeds an audio signal S1 obtained as a result to the controller 10.The CCD camera 16 photographs the surroundings and feeds an image signalS2 to the controller 10.

[0025] Furthermore, the touch sensor 17 detects a pressure generatedupon receiving a physical action such as ‘Stroke’ or ‘pat’ by the user,and the result of which is fed to the controller 10 as a pressuredetection signal S3. The distance sensor 18 measures the distance to anobject ahead, and the result of which is also fed to the controller 10as a distance measurement signal 4.

[0026] Thus the external sensor 19 generates an external informationsignal S5 consisting of the audio signal S1, image signal S2, pressuredetection signal S3 and distance measurement signal S4 generated basedon the information outside the pet robot 1, which (the externalinformation signal S5) is fed to the controller 10.

[0027] Meanwhile, the battery sensor 12 of the internal sensor 14detects the residual charge of the battery 11, and the result of whichis fed to the controller 10 as a battery residue detection signal S6.The thermal sensor 13 detects a temperature inside the pet robot 1, andthe result of which is fed to the controller 10 as a thermal detectionsignal S7.

[0028] Thus the internal sensor 14 generates an internal informationsignal S8 consisting of a battery residue detection signal S6 andthermal detection signal S7 generated based on the information obtainedinside the pet robot 1, which (the internal information signal S8) isfed to the controller 10.

[0029] The controller 10 judges the surroundings and its own condition,and if there are directions or actions from the user, based on theexternal information signal S5 fed from the external sensor 19 and theinternal information signal S8 fed from the internal sensor 14.

[0030] Furthermore, the controller 10 determines a subsequent actionbased on the result of judgment and the control program stored in thememory 10. Then, actuators 21A˜21N are driven based on the above resultto perform such actions as making the head unit 4 swing up and down,left to right, the tail 5A of the tail unit 5 wag and the leg units3A˜3D walk.

[0031] The controller 10 feeds an audio signal 9 to the speaker 20 whennecessary in the process, and outputs a voice corresponding to the audiosignal 9. Besides the controller 10 makes an LED (Light Emitting Diode,not shown in figure)) turn on or off, or blink, which is placed at theposition of the ‘eye’ of the pet robot 1.

[0032] Thus the pet robot is designed to be able to act autonomouslybased on the surroundings, its own condition and directions and actionsfrom the user.

[0033] (2) What the controller 10 Processes

[0034] Now, explanations is given on what the controller 10 processesconcretely, relative to action generation of the pet robot 1.

[0035] As shown in FIG. 3, what the controller 10 processes basically,relative to action generation of the pet robot 1, can be divided intothe following in terms of functions; a) a condition recognitionmechanism 30 which recognizes external and internal conditions; b) anemotion/instinct modeling mechanism 31, and c) an action determiningmechanism 32 which determines a subsequent action based on a recognitionresult obtained by the condition recognition mechanism 30; d) an actiongeneration mechanism 33 which actually makes the pet robot 1 act basedon a decision made by the action determining mechanism 32.

[0036] Detailed description will be given hereunder on the conditionrecognition mechanism 30, emotion/instinct modeling mechanism 31, actiondetermining mechanism 32, and the action generation mechanism 33.

[0037] (2-1) Configuration of the condition recognition mechanism 30

[0038] The condition recognition mechanism 30 recognizes a particularcondition based on an external information signal S5 fed from theexternal sensor 18 (FIG. 2) and an internal information signal S8 fedfrom the internal sensor 14 (FIG. 2), the result of which is conveyed tothe emotion/instinct modeling mechanism 31 and the action determiningmechanism 32.

[0039] In practice the condition recognition mechanism 30 constantlywatches for an image signal S2 (FIG. 2) coming from the CCD camera 8(FIG. 2) of the external sensor 19, and if and when, a ‘red, roundthing’ or a ‘perpendicular plane’, for example, has been detected in animage produced based on the image signal S2, it is judged that ‘There isa ball.’ or that ‘There is a wall.’, and the result recognized isconveyed to the emotion/instinct modeling mechanism 31 and the actiondecision-making mechanism 32.

[0040] Also, the condition recognition mechanism 30 constantly watchesfor an audio signal S1 (FIG. 2) coming from the microphone 15 (FIG, 2),and if and when it recognizes direction sounds such as ‘Walk’, ‘Liedown’ or ‘Follow the ball’ based on the audio signal S1, the resultrecognized is conveyed to the emotion/instinct modeling mechanism 31 andthe action determining mechanism 32.

[0041] Furthermore, the condition recognition mechanism 30 constantlywatches for a pressure detection signal S3 (FIG. 2) coming from thetouch sensor 17 (FIG. 2), and if and when a pressure has been detectedfor a short period of time (e.g., less than 2 seconds) and longer than agiven period of time, based on the pressure detection signal S3, it isrecognized as ‘Hit’ (Scolded), while if and when a pressure has beendetected for a longer period of time (e.g., longer than 2 seconds) andless than a given threshold value, it is recognized as ‘Stroked’, andthe result of recognition is conveyed to the emotion/instinct modelingmechanism 31 and the action determining mechanism 32.

[0042] Yet furthermore, the condition recognition mechanism 30constantly watches for a temperature detection signal S7 (FIG. 2) comingfrom the temperature sensor 13 (FIG. 2) of the internal sensor 14 (FIG,2), and if and when a temperature greater than a given value is detectedbased on the temperature detection signal S7, it is recognized that ‘theinternal temperature has risen’, and the result of recognition isconveyed to the emotion/instinct modeling mechanism 31 and the actiondetermining mechanism 32.

[0043] (2-2) Configuration of the emotion/instinct mechanism 31

[0044] The emotion/instinct modeling mechanism 31 has a parameter toindicate the intensity of each of a total of six emotions; ┌Joy┘,┌Grief┘, ┌Surprise┘, ┌Fear┘, ┌Dislike┘, and ┌Anger┘. And theemotion/instinct modeling mechanism 31 alters a parameter value of eachemotion in order, based on condition recognition information S11, aparticular recognition result, such as ‘Hit’ or ‘Stroked’, fed from thecondition recognition mechanism 30, and action determining informationS12 indicating a determined subsequent output action, supplied from theaction determining mechanism 32 (to be described in detailhereinafter.), and a laps of time.

[0045] Concretely, the parameter value E[t+1] of an emotion to occur inthe next period is calculated in a given period at the emotion/instinctmodeling mechanism 31 using the following expression;

E[t+1]=E[t]+K _(e) ×ΔE[t]  (1)

[0046] where, ΔE [t] is a variation of a particular emotion calculatedusing a given operation expression based on an extent (preset) of arecognition result based on the condition recognition information S11and an output action based on action determining information S12 workingon the particular emotion, and based an extent of a restraint and animpetus received from other emotions, and a lapse of time;

[0047] E [t] is the a parameter value of a current emotion;

[0048] K_(e) is a coefficient indicating a ratio for varying the emotionbased on a recognition result and other factors.

[0049] Thus, the emotion/instinct modeling mechanism 31 alters theparameter value of the emotion by replacing the parameter E [t] of thecurrent emotion with the above operation result.

[0050] It should be noted that it is predetermined that parameters ofwhat emotions should be altered according to each recognition result oroutput action. For example, given a recognition result of ‘Hit’, theparameter value of emotion ‘Angry’ increases while that of emotion ‘joy’decreases. Another example: Given a recognition result ‘Stroked’, theparameter value of emotion ‘joy’ increases while that of emotion ‘Grief’decreases.

[0051] Likewise, the emotion/instinct modeling mechanism 31 has aparameter to show the intensity of each of four (4) desires, i.e.,‘desire for moving’, ‘desire for affection’, ‘desire for appetite’, and‘curiosity’, which are all independent of each other. And theemotion/instinct mechanism 31 alters the values of these desires inorder, based on a recognition result conveyed from the conditionrecognition mechanism 30, lapse of time, and a notice from the actiondetermining mechanism 32.

[0052] Concretely, as to ‘desire for moving’, ‘desire for affection’ and‘curiosity’, the value of a parameter I[K+1] of a desire to occur in thenext period is calculated at the emotion/instinct modeling mechanism 31,using the following expression;

I[k+1]=I[k]+K ₁ ×ΔI[k]  (2)

[0053] where, ΔI [k] is a variation quantity of a particular desirecalculated using a predetermined operation expression based on an outputaction of the pet robot 1, a lapse of time, and a recognition result;

[0054] I[k] is the value of a parameter of the desire obtained as aresult of a subtraction;

[0055] k₁ is a coefficient indicating the intensity of the desire.Therefore, the parameter value of a particular desire is altered in away that the current parameter value I[k] of the desire is replaced withthe above operation result.

[0056] It should be noted that it is predetermined what parameter ofemotion should be varied against an output action or a recognitionresult. For example, if and when notified from the action determiningmechanism 32 that some action has taken place, the parameter value of‘desire for moving’ decreases.

[0057] Regarding the ‘desire for appetite’, the emotion/instinctmodeling mechanism 31 calculates a parameter value I[k+1] of ‘desire forappetite’, using the following expression;

I[k]=100−B _(L)   (3)

[0058] where, B_(L) is the residual charge of the battery based on abattery residue detection signal S6 (FIG. 2) fed through the conditionrecognition mechanism 30. Thus, the parameter value of the ‘desire forappetite’ is altered in a way that the current parameter value I[k] ofthe desire is replaced with the above operation result.

[0059] In this mode of carrying out the present invention the parametervalue of each emotion or desire is regulated so as to vary in a range offrom 0 to 100. Also, the coefficients k_(e) and k₁ are set individuallyfor each emotion and desire.

[0060] (2-3) Configuration of the action determining mechanism 32

[0061] The action determining mechanism 32 determines a subsequentaction based on the condition recognition information S1 supplied fromthe condition recognition mechanism 30, a parameter value of eachemotion and desire at the emotion/instinct modeling mechanism 31, actionmodels stored in the memory 10A in advance, and a lapse of time, and itis putout at the emotion/ instinct modeling mechanism 31 and the actiongeneration mechanism 33 as action determining information 12.

[0062] In this instance, the action determining mechanism 32 utilizes analgorithm called ‘probability automaton’ as a means to determine asubsequent action.

[0063] With this algorithm, as shown in FIG. 4, a decision is made uponwhether the current NODE₀, (condition) should remain where it is or itshould transit to one of the other NODE₀˜NODE_(n), with probabilitybased on a transition probability P₀˜P_(n) set to ARC₀˜ARC_(n)connecting each NODE₀˜NODE_(n) .

[0064] More concretely a condition transition table 40 as shown in FIG.5, is stored for each NODE₀˜NODE_(n) in the memory 10 as an action modelso that the action determining mechanism 32 can make a decision on asubsequent action based on this condition transition table 40.

[0065] In this condition transition table 40, input events (recognitionresults at the condition recognition mechanism 30) or transitionconditions at the current NODE₀˜NODE_(n) are enumerated on the ┌InputEvent┘ line with priority, and further conditions to the abovetransition conditions are defined on the column corresponding to theline of the ┌Name of Data┘ and ┌Range of Data┘.

[0066] Accordingly, with a NODE₁₀₀ defined in the condition transitiontable 40 per FIG. 5, the conditions for the current node to remain whereit is or to transit to another node are; in case a recognition resultthat ┌A ball has been detected┘ is given, that very existence and thefact that the size of the ball is within the range of from 0 to 1000 (0,1000); and in case a recognition result that ┌An obstacle has beedetected┘ is given, this recognition result itself and the fact that thedistance to the obstacle is within the range of from 0 to 1000 (0,1000).

[0067] Also, even if there is no input of a recognition result atNODE₁₀₀, if, of the parameter values of each emotion or desire at theemotion/instinct modeling mechanism 31, to which the action determiningmechanism 32 periodically refers, a parameter value of any of emotions‘joy’, ‘Surprise’, or ‘Grief’ is within a range of from 50 to 100 (50,100), the current node may remain where it is or transit to anothernode.

[0068] Furthermore, in the condition transition table 40, the names ofnodes which can transit from the NODE₀˜NODE_(n), to the row ┌Node toFollow┘ are enumerated in the column of ┌Transition Probability to OtherNodes┘. Simultaneously a transition probability to the thenNODE₀˜NODE_(n) available when all the conditions defined on each line of┌Name of Input Event┘, ┌Value of Data┘, and ┌Range of Data┘ are met, isdescribed on the line of the then NODE₀˜NODE_(n) in the column┌Transition Probability to Another Node┘. And an action or operationputout at this moment is described on the line ┌Output Action┘. The sumof the transition probability on each line in the column ┌TransitionProbability to Another Node┘ should be 50[%].

[0069] Accordingly, at the NODE₁₀₀ in this example, if, for example, arecognition result that ┌a ball has been detected ┌BALL┘ and that the┌SIZE┘ is in the range of ┌from 0 to 1000 (0, 1000) are given, it cantransit to NODE₁₂₀ (node 120) with a probability of ┌30[%]┘, and anaction or operation of ┌ACTION 1┘ is output at this moment.

[0070] And this action model is configured in a way that a number ofNODE₀˜NODE_(n) described as the Condition Transition Table 40 are linkedtogether.

[0071] Thus, when condition recognition information S11 is supplied fromthe condition recognition mechanism 30 or when a given lapse of timeexpires since the last action was discovered, the action determiningmechanism 32 determines an subsequent action or operation (an action oroperation described on the ┌Output Action┘ line) with probability usingthe Condition Transition Table 40 of corresponding NODE₀˜NODE_(n) of theaction models stored in the memory 10A, and the decision result isoutput at the emotion/instinct modeling mechanism 31 and the actiongeneration mechanism 33 as action determining information S12.

[0072] (2-3) Processing of the action generation mechanism 33

[0073] Then, the action generation mechanism 33 generates a controlsignal S10A˜S10N for each actuator 21A˜21N needed based on the actionplan formed in the preceding process. Then, the actuators 21A˜21Nrequired are driven and controlled based on these control signalsS10A˜S10N to make the pet robot 1 carry out an action determined by theaction determining mechanism 32.

[0074] Also, when action determining information D2 such as ‘Bark’ or‘Turn on the LED for the eye’ is fed from the action determiningmechanism 32, the action generation mechanism 33 outputs a voice basedon a voice signal S9 by feeding said predetermined voice signal S9 tothe speaker 20, or makes the LED blink by feeding or ceasing to feed adriving voltage to said LED located where an eye is supposed to be.

[0075] As described above, the controller 10 controls each actuator21A˜21N and a voice output so that the pet robot 1 can act autonomouslybased on external information signal S5 supplied from the externalsensor 19 and internal information signal S8 supplied from the internalsensor 14.

[0076] (2-4) Processing of the Action Control Mechanism 34

[0077] In addition to the mechanisms explained so far, the pet robot 1has an action control mechanism 34, another function relating to actiongeneration. Condition recognition information 11 and an externalinformation signal S5 are fed to this action control mechanism 34 fromthe condition recognition mechanism 30 and the external sensor 19respectively.

[0078] The action control mechanism 34 generates the pattern of aperson's face (which is hereinafter referred to as the face pattern)based on an image signal S2 fed from the CCD camera 16 when it receivesthe condition recognition information S11 that ‘there exists a person’from the condition recognition mechanism 30, and this face pattern isstored in the memory 10A.

[0079] At this moment the action control mechanism 34 compares the facepattern to be entered into the memory 10A with each data of facepatterns previously stored in the memory 10A and only a few facepatterns which appear mostly frequently are retained in the memory 10A.

[0080] Also, the action control mechanism 34 generates an emotion table35 (35A˜35J) consisting of counter tables for counting, for example, theintensity of ‘Friendliness’ or ‘Dislike’ of a particular person,comparing his face pattern with each face pattern stored in the memory10A as shown in FIG. 6, and this emotion table 35 is stored into thememory 10A.

[0081] Then the action control mechanism 34 varies in order the countvalue of ‘Friendliness’ or ‘Dislike’ corresponding to the emotion table35 of that person, according to actions such as ‘Hit’ or ‘Stroke’performed by the person whose face pattern data is stored in memory 10A.

[0082] For instance, the action control mechanism 34 works in a waythat, given the condition recognition information S11 of the previouslyappointed favorable actions or calls such as ‘Praised’, ‘Stroked’,‘Charged’, or ‘Played together’ in a condition where it is recognizedthat there is a person nearby whose face resembles that of any facebased on the face patterns stored in the memory 10, the count value forthe intensity of ‘Friendliness’ on the emotion table 35 of therecognized face pattern increases by a preset quantity, while the countvalue for the intensity of ‘Dislike’ decreases by a preset quantity.

[0083] The increase in this instance is preset depending on the contentof an action or a call, for example, ┌1┘ for ‘Praised’ or ‘Stroked’, and┌2 ┘ for ‘charged’ or ‘Played together’, according to the content of anaction or a call.

[0084] Likewise, the action control mechanism 34 works in a way that,given the condition recognition information S11 of previously appointedill-intentioned actions such as ‘Scolded’, ‘Hit’, ‘Request for chargingignored’, or ‘Request for playing together ignored’ in a condition whereit is recognized that there is a person nearby whose face resembles thatof any face based on the face patterns stored in the memory 10, thecount value for the intensity of ‘Dislike’ on the emotion table 35 ofthe recognized face pattern increases by a preset quantity, while thecount value for the intensity of ‘Friendliness’ decreases by a presetquantity.

[0085] In this instance, too, an increase is preset according to thecontent of an action or a call, for example, [1] for ‘Scolded’, or‘Hit’, and [2] for ‘Request for charging ignored’, or ‘Request forplaying together ignored’ according to the content of an action or acall.

[0086] Thus, the action control mechanism 34 counts the value of theintensity of ‘Friendliness’ or ‘Dislike’ for a few persons who appearmost frequently using the emotion table 35.

[0087] The action control mechanism 34 makes or alters such an emotiontable 35 at this moment, according to the emotion tableformation/alteration process procedure RT1 as shown in FIG. 7.

[0088] That is, the action control mechanism 34, given a recognitionresult that ‘There exists a person nearby’, starts the emotion tableformation/alteration process procedure RT1 at the step SP1 and proceedsto the next step SP2 to recognize the face pattern of an object based onan image signal S2 supplied from the CCD camera 16.

[0089] Subsequently the action control mechanism 34 proceeds to the nextstep SP3, where the face pattern of the object recognized at the stepSP2 is compared with the face pattern of each face pattern data storedin the memory 10 and judged whether or not there exists an identicalface pattern.

[0090] If a negative result is obtained at the step SP3, the actioncontrol mechanism 34 proceeds to the step SP4, where an emotion table 35is made for the new face pattern (based on the newly-made emotion table35). Then this table 35 and the face pattern corresponding to it, arestored in the memory 10 in a way that they replace the face pattern andemotion table 35 of a person who appears least frequently of 10 personsstored in the memory 10 who appear most frequently, and then the actioncontrol mechanism 34 proceeds to the step SP6. Simultaneously the actioncontrol mechanism 34 sets the count value of the intensity of‘Friendliness’ or ‘Dislike’ on the emotion table 35 to a predeterminedinitial value respectively.

[0091] To the contrary, If an affirmative result is obtained at the stepSP3, the action control mechanism 34 proceeds to the step SP5, and afterretrieving a corresponding table 35, it proceeds to the step SP6.

[0092] The action control mechanism 34 judges at this step SP6 whetheror not there exists a call such as ‘Praise’ or ‘Scold’, or an actionsuch as ‘Stroke’ or ‘Hit’, and if a negative result is obtained,proceeds to the step SP10 and terminates this emotion tablemaking/alteration process procedure RT1.

[0093] To the contrary, if an affirmative result is obtained at the stepSP6, the action control mechanism 34 proceeds to the step SP7 and judgeswhether or not the result is a predetermined favorable action such as‘Praise’ or ‘Stroke’.

[0094] If an affirmative result is obtained at this step SP7, the actioncontrol mechanism 34 proceeds to the step SP8, and increases the valueof the intensity of ‘Friendliness’ on the new emotion table 35 made atthe step SP4 or the value on the emotion table 35 retrieved at the stepSP5 by as much value as corresponding to an action exerted by thatperson, and at the same time decreases the value of ‘Dislike’ by asmuch. Subsequently the action control mechanism 34 proceeds to the stepSP10 and terminates this emotion table making/alteration processprocedure RT1.

[0095] To the contrary, If a negative result is obtained at this stepSP7, the action control mechanism 34 proceeds to the step SP9 anddecreases the value of the intensity of ‘Friendliness’ on the newemotion table 35 made at the step SP4 or the value on the emotion table35 retrieved at the step SP5 by as much value as corresponding to anaction exerted by that person, and at the same time increases the valueof ‘Dislike’ by as much. Subsequently the action control mechanism 34proceeds to the step SP10 and terminates this emotion tablemaking/alteration process procedure RT1.

[0096] Thus, the action control mechanism 34 forms the emotion table 35,and at the same time alters the emotion table 35 in order according toan action made by that person.

[0097] Also, the action control mechanism 34 controls the actiongeneration of the pet robot 1 according to an action control processprocedure RT2 as shown in FIG. 8, in parallel with the above processing.

[0098] That is, the action control mechanism 34, immediately after thepower is turned on, starts the action control process procedure RT2 atthe step SP11, and judges at the subsequent steps SP12 and SP13 in orderwhether or not there exists a voice call, or whether or not there existsa person nearby based on the condition recognition information S11supplied from the condition recognition mechanism 30. The action controlmechanism 34 repeats an SP12-SP13-SP12 loop until an affirmative resultis obtained at either SP12 or SP13.

[0099] And, when and if an affirmative result is obtained at the stepSP12 in due course, the action control mechanism 34 proceeds to the stepSP14 and determines the direction in which a voice originates, and thenproceeds to the step SP16. Also, upon obtaining an affirmative result atthe step SP13 the action control mechanism 34 proceeds to the step SP15and determines the direction in which a person is recognized by thecondition recognition mechanism 30, and then proceeds to the step SP16.

[0100] Next, the action control mechanism 34 controls the actiondetermining mechanism 32 at the step SP16 so that the pet robot 1 movestoward a person who originates a call or a person recognized by thecondition recognition mechanism 30. (Such a person is hereinafterreferred to as ‘Object’.)

[0101] In practice such a control can be accomplished by assigning 100[%] for a transition probability P₁ to NODE₁ (e.g., ‘Walk’)corresponding to the probability automaton shown in FIG. 4, and 0 [%]for a transition probability to another NODE₂˜NODE_(n).

[0102] The action control mechanism 34 proceeds to the step SP7 andmeasures the distance to an object based on a distance measuring signalS14 supplied from a distance sensor 18, then proceeds to the step SP18,where it is judged if the distance measured comes to be equal to thepreset distance, or whether or not the face of the object comes to bewithin an range in which it is recognizable.

[0103] If a negative result is obtained at the step SP18, the actioncontrol mechanism 34 returns to the step SP16 and repeats anSP16-SP17-SP18-SP16 loop until an affirmative result is obtained at thestep SP18.

[0104] And, the action control mechanism 34 proceeds to the step SP19upon obtaining an affirmative result at the step SP18 and executes theaction determining process procedure RT3 shown in FIG. 9.

[0105] In practice, when the action control mechanism 34 proceeds to thestep SP19 of the action control processing procedure RT2, this actiondetermining processing procedure RT3 (FIG. 9) starts at the step SP31,and then the face pattern of an object is recognized at the followingstep SP32, based on the image signal S2 fed from the CCD camera 16.

[0106] Subsequently the action control mechanism 34 proceeds to the stepSP33, and compares the face pattern of the object recognized at the stepSP32 with a face pattern based on each face pattern data stored in thememory 10A.

[0107] If the face pattern of the object coincides with a face patternbased on any face pattern data stored in the memory 10A, the actioncontrol mechanism 34 reads out the count value of the intensity of eachof ‘Friendliness’ or ‘Dislike’ from a suitable emotion table 35 storedin the memory 10A.

[0108] Then, the action control mechanism 34 proceeds to the step SP34and decides whether or not the pet robot 1 likes the object based on avalue of ‘Friendliness’ and a value of ‘Dislike’. In practice thisdecision is made in a way that if the count value of the intensity of‘Friendliness’ is greater than or equal to that of ‘Dislike’, the objectis judged as friendly. To the contrary, if the count value of theintensity of ‘Dislike’ is greater than that of ‘Friendliness’, theobject is judged as unfavorable.

[0109] Furthermore the action control mechanism 34 proceeds to the stepSP35 and alters a variation in the parameter value of the emotions andthe desires retained in the emotion/instinct modeling mechanism 31 and atransition probability in a condition transition table retained in theaction determining mechanism 32, based on a judgment result obtained atthe step SP36.

[0110] Then the action control mechanism 34, when varying the parametervalue of emotion and that of desire retained in the emotion/instinctmodeling mechanism 31, alters a variation in each parameter valueeffected depending upon how it (the pet robot 1) is ‘Hit’ or ‘Stroked’,according to the judgment result obtained.

[0111] In practice, the emotion/instinct mechanism 31 calculates aparameter value of emotion, using the following expression:

E[t+1]=E[t]+K _(e) ×ΔE[t]  (1)

[0112] and a parameter value required is calculated, using the followingexpression:

I[k+1]=I[k]+k _(l) ×ΔI[k]  (2)

[0113] Therefore, the action control mechanism 34 can alter a variationin the parameter values obtained by means of the above expressions byvarying the coefficient K_(e) and K_(I) in the above expressions,according to the judgment result.

[0114] Therefore, the action control mechanism 34, when a recognitionresult, e.g., ‘Stroked’ is obtained in case the object is judged asfriendly, gives a large value to the coefficient K_(e) of the expressionwith use of which a parameter value of an emotion ‘joy’ is calculated.Whereas, a small value is given to the coefficient K_(e) of theexpression with use of which a parameter value of an emotion ‘angry’ iscalculated. In this manner a parameter value of ‘joy’ for a friendlyobject can be increased by ‘2’, which usually increases by only ‘1’while the parameter value of ‘angry’ can be decreased by only ‘0.5’,which usually decreases by ‘1’.

[0115] To the contrary, the action control mechanism 34, when arecognition result, e.g., ‘Hit’, is obtained in case the object isjudged as unfavorable, gives a large value to the coefficent K_(e) ofthe expression with use of which the parameter value of an emotion ‘joy’is calculated, and gives a large value, too, to the coefficient K_(e) ofthe expression with use of which the parameter value of an emotion‘angry’ is calculated. In this manner the parameter value of ‘joy’ foran unfavorable object can be decreased by ‘2’, which usually decreasesby only ‘1’ while the parameter value of ‘angry’ can be increased by‘2’, which usually increases by only ‘1’.

[0116] Meantime, if the transition probability of an action modelretained in the action determining mechanism 32 is varied and if thereoccurs a concrete action as described above, the action controlmechanism 34 alters, according to such a judgment result, a transitionprobability defined in the column of ┌Transition Probability to AnotherNode┘ in the condition transition table as shown in FIG. 5.

[0117] And, the action control mechanism 34 can alter an action oroperation to be generated according to the judgment result, in a waythat if the object is judged as friendly, a transition probabilitycapable of transiting to a node where an action or operation of, e.g.,‘joy’ is made, increases from ‘50 [%]’ to ‘80 [%]’, whereas if theobject is judged as unfavorable, a transition probability capable oftransiting to a node where an or operation of e.g., ‘joy’ is made,decreases from ‘50 [%]’ to ‘30 [%]’.

[0118] Thus, in practice, as shown in FIG. 10, the action determiningmechanism 32 makes it easier to generate an action of ‘Rejoiced’ if theobject is judged as friendly, by increasing, e.g., a transitionprobability P₁₂ to transit from ‘angry’ node 51 to ‘Rejoiced’ node 52, atransition probability₂₂ to transit from ‘Rejoiced’ node 52 to‘Rejoiced’ node 52 of its own, and a transition probability P₃₂ totransit from ‘Walk’ node 53 to ‘Rejoiced’ node 52 respectively.

[0119] Also, the action determining mechanism 32 makes it easier togenerate an action of ‘Get angry’ if an object is judged as unfavorable,by increasing, e.g., a transition probability P₁₁ to transit from ‘Getangry’ node 51 to ‘Get angry’ node 51 of its own, a transitionprobability₂₁ to transit from ‘Rejoiced’ node 52 to ‘Get angry’ node 51,and a transition probability P₃₁ to transit from ‘Walk’ node 53 to ‘Getangry’ node 51.

[0120] The action control mechanism 34 proceeds to the step 35 andterminates this action determining processing procedure RT13, andfurther proceeds to the step SP20 for the action control processingprocedure RT2, the main routine.

[0121] Then, the action control mechanism 34 judges at this step SP20whether or not the object is friendly based on the decision resultobtained at the step SP19.

[0122] Concretely, if an affirmative result is obtained at this stepSP20, the action control mechanism 34 proceeds to the SP21 and controlsthe action determining mechanism 32 so that the pet robot 1 takes anaction to approach the object, showing its good temper by singing a songor wagging the tail 5A.

[0123] At this instance the action control mechanism 34 controls theaction determining mechanism 32 and the action generation mechanism 33so that the greater the difference between the intensity of‘Friendliness’ and that of ‘Dislike’, so much the better the temper ofthe pet robot 1 seems to be. (For example, the wagging of the tail 5A orthe speed of approaching is accelerated by varying the speed of rotationof the actuators 21A˜21N.)

[0124] At this moment, the action control mechanism 34 makes it easierto generate an action or operation made by an emotion ‘joy’ of the petrobot 1 in a way that a variation in the parameter value of emotion andthat of desire are varied according to the judgment result of whether ornot the object is friendly obtained at the step 35.

[0125] To the contrary, the action control mechanism 34 proceeds to thestep SP22 if a negative result is obtained at the step SP20, andcontrols the action determining mechanism 32 so that the pet robot 1goes away from the object.

[0126] (3) Learning and Recognition Processing at Action ControlMechanism 34

[0127] Elucidation is given on how to learn, recognize, and process facepatterns at the action control mechanism 34.

[0128] With the pet robot 1, a method disclosed in the Japanese OfficialBulletin TOKKAIHEI Issue NO. 6-89344 is used for the action controlmechanism 34 as a means for learning, recognizing and processing facepatterns. Concretely the action control mechanism 34 incorporates a facepattern learning, recognizing, and processing units 40 in it.

[0129] In this face pattern learning, recognizing, and processing units40, the face part of the image signal S2 fed from the CCD camera 16 isquantified in e.g., 8 bits at the memory 41 comprising a RAM (RandomAccess Memory) and an analog/digital converter, and a face pattern dataI (x, y) consisting of a quadratic brightness data obtained on an xyplane is memorized by the frame in the RAM of the memory 41.

[0130] A front processor 42 does a pre-processing like detecting, e.g.,edges in the face image date I (x, y) memorized in the memory 41 andretrieves a face pattern P (x, y) as the quantity of characteristics ofthe face image [face image data I (x, y)], which is conveyed to acomparison processor 43.

[0131] The comparison processor 43 calculates a contribution degreeX_(i) consisting of a correlation quantity to the face pattern P (x, y)for each of the r number of functions Fi (x, y) (i =1,2,. . . , r)memorized as the basic model of the face pattern P (x, y) beforehand ina function learning memory 44.

[0132] Also, the comparison processor 43 detects a function F_(MAX) (x,y) having the maximum contribution degree X_(MAX) (1≦MAX≦r), and thisfunction F_(MAX) (x, y) or the face pattern P (x, y) is transformeduntil the contribution degree X_(MAX) of this function F_(MAX) (x, y)becomes the greatest or reaches the maximum point. In this way, atransformation quantity M (x, y) composed of the difference between thefunction F_(MAX) (x, y) and the face pattern P (x, y) is calculated.

[0133] This transformation quantity M (x, y) is fed to a functionlearning memory 44 and a transformation quantity analyzer 45. The facepattern P (x, y) is fed to the function learning memory 44, too.

[0134] Composed of e.g., neural networks, the function learning memory44 memorizes the r number of functions Fi (x, y) (i=1,2, . . . , r) asthe basic model of the face pattern P(x, y) as described above.

[0135] The function learning memory 44 transforms the function F_(MAX)(x, y) or the face pattern P (x, y) using the transformation quantity M(x, y) supplied and alters the function F_(MAX) (x, y) based on atransformed function F_(MAX)′ (x, y) on the xy plane and a transformedface pattern P′ (x, y).

[0136] The transformation analyzer 45 analyzes the transformationquantity M (x, y) fed from the comparison processor 43 and generates anew transformation quantity Mtdr (x, y) by removing dissimilarities, atthe top and bottom, left and right, in the face pattern P (x, y) in theimage, a slippage due to rotations, or differences in size due to thedistance and the ratio of magnification or reduction. Thistransformation quantity Mtdr (x, y) is fed to a character informationlearning memory 46.

[0137] In case the learning mode is in operation, the characterinformation learning memory 46 stores into the internal memory (notshown in figure) the transformed quantity Mtdr (x, y) to be fed , whichis related to the character information K (t) or the function of thenumber t (t=1, 2, . . . T; T=the number of faces of the characters)assigned to e.g., a character (face). (For example, the average value ofa plurality of transformed quantities Mtdr(x,y), Mtdr′ (x,y), Mtdr″(x,y) . . . of the face image of the same character t is assumed as thecharacter information K (t).]

[0138] In other words, if the learning mode is in operation, thetransformation quantity Mtdr (x, y) itself of the character outputtedfrom the transformation quantity analyzer 45 is memorized in thecharacter information learning memory 46 as the character information ,and whenever the transformation quantity Mtdr (x, y) of the samecharacter t is inputted later, the character information K (t) isaltered based on the transformation quantity Mtdr (x, y).

[0139] Furthermore, if the recognition mode is in operation, thecharacter information learning memory 46 calculates a transformationquantity Mtdr (x, y) to be fed from the transformation quantity analyzer45 and e.g., an Euclidean distance to each character information K (t)memorized beforehand in the internal memory and outputs as a recognitionresult the number t in the character information K (t) which makes thedistance the shortest.

[0140] In the face pattern learning and recognition processor 40 thusconfigured, the transformation quantity M (x, y) is analyzed in thetransformation quantity analyzer 45, and a parallel move component, arotational move component, and an enlargement/reduction componentcontained in the transformation quantity M (x, y) are removed so as toalter the standard pattern memorized in the character informationlearning memory 46 based on a new transformation quantity Mtdr (x, y).Therefore, a high level of the recognition ratio is accomplished.

[0141] (4) Operation and effect of this embodiment

[0142] With the configuration as described heretofore, the pet robot 1memorizes not only the face patters of a few people who appear the mostfrequently, but also new face patterns replacing the face patterns of afew people who appear the least frequently upon obtaining new facepatterns.

[0143] Also, the pet robot 1 counts the intensity of ‘Friendliness’ or‘Dislike’, according to the history of information entered on actions orcalls taken and made toward the pet robot 1 by a person, comparing thatperson with such a face pattern data.

[0144] The pet robot 1, when called or if a person has been detectednearby, approaches that person, performing a friendly behavior when theintensity of ‘Friendliness’ is larger than that of ‘Dislike’ or thethreshold value.

[0145] The pet robot 1, however, goes away from that person if theintensity of ‘Friendliness’ is smaller than that of ‘Dislike’ or thethreshold value, or if the pet robot 1 does not have that person's facepattern data in memory.

[0146] Thus, the pet robot 1 performs natural actions or operations likea living thing as if it were a real animal, and approaches a personwhose degree of ‘Friendliness’ is high based on the history ofinformation entered of that person's actions and calls, or goes awayfrom a person whose degree of ‘Friendliness’ is low or from a personwhom the pet robot does not know. Consequently the pet robot 1 iscapable of giving a sense of affinity and satisfaction to the user.

[0147] With the configuration described heretofore, the pet robot 1detects a person based on an external information signal S5 fed from theexternal sensor 19 and judges if the intensity of ‘Friendliness’ isgreater or smaller than that of ‘Dislike’ by a given value. When theanswer is ‘greater’, the pet robot 1 approaches that person, and goesaway from that person if the answer is any other than ‘greater’. Thus,the pet robot 1 is made to perform natural actions or operations like aliving thing, thereby succeeding in giving a sense of affinity andsatisfaction to the user. Now, a pet robot which is capable of enhancingthe entertaining quality for the user, can be realized.

[0148] (5) Other modes of carrying out the present invention

[0149] In the above mode of carrying out the present invention,elucidation is given on the case, wherein the present invention isapplied to the pet robot configured as shown in FIGS. 2 and 3. However,the present invention is not limited to it, and applicable widely torobots with a variety of other configurations and control methods forthem.

[0150] Again in the above mode of carrying out the present invention,elucidation is give on the case, wherein the intensity of ‘Friendliness’or ‘Dislike’ is counted based on calls such as ‘Praise’ and ‘Scold’ andon actions such as ‘Stroke’ and ‘Hit’. However, the present invention isnot limited to it, and other calls and actions such as ‘Played together’and ‘Ignored’ may as well be used as elements for counting the intensityof ‘Friendliness’ and ‘Dislike’.

[0151] Furthermore, in the above mode of carrying out the presentinvention, elucidation is given on the case, wherein the pet robot 1 perse chooses an object (human being), of which degree of ‘Friendliness’and ‘Dislike’ is counted, and the present invention is not limited toit, and the user may as well set an object for counting the intensity of‘Friendliness’ and ‘Dislike’.

[0152] Furthermore, in the above mode of carrying out present invention,elucidation is given on the case, wherein the pet robot 1 approaches theuser upon being spoken to by the user by means of a voice. However, thepresent invention is not limited to it, and it may as well be such thatthe pet robot approaches the user upon being spoken to by means of anultrasonic wave or a sound command that outputs directions expressed insound.

[0153] Furthermore, in the above mode of carrying out the presentinvention, elucidation is given on the case, wherein a person nearby isdetected based on an image signal S2 through the CCD camera 16. However,the present invention is not limited to it, and other means than suchvisual information such as an odor or environmental temperature may aswell be used for detecting a person nearby.

[0154] Furthermore, in the above mode of carrying out the presentinvention, elucidation is given on the case, wherein the user isrecognized based on the face pattern. However, the present invention isnot limited to it, and other elements or features such as a ‘voice’,‘odor’, ‘environmental temperature’, or ‘physique’, etc., too, can beutilized to detect the user, in addition to the face pattern. This canbe realized by putting data on a ‘voice’, ‘odor’, ‘environmentaltemperature’, and ‘physique’, etc. into the pet robot 1 in advance.

[0155] Furthermore, in the above mode of carrying out the presentinvention, elucidation is given on the case, wherein there are only twoaction patterns based on the intensity of ‘Friendliness’ and ‘Dislike’;the pet robot 1 approaches a person when the intensity of ‘Friendliness’is greater than that of ‘Dislike’ by a given value; otherwise it goesaway from the person. However, the present invention is not limited toit, and a plurality of other action patterns may be incorporated into arobot, for example, an action pattern, in which a pet robot runs awaywhen the intensity of ‘Dislike’ is greater than that of ‘Friendliness’by a given value.

[0156] Furthermore, in the above mode of carrying out the presentinvention, elucidation is given on the case, wherein an object the petrobot 1 approaches or goes away from, is a human being. However, thepresent invention is not limited to it, and it may as well be such thatpreset actions or operations are made responding to colors, sounds,physical solids, animals, or odors.

[0157] Furthermore, in the above mode of carrying out the presentinvention, elucidation is given on the case, wherein the external sensor19 as an external information read-in means for reading in informationfrom the outside, comprises the microphone 15, CCD camera 16, touchsensor 17 and distance sensor 18. However, the present invention is notlimited to it, and the external sensor 19 may as well be composed ofother disparate sensors in addition to them, or other disparate sensorsonly.

[0158] Furthermore, in the above mode of carrying out the presentinvention, elucidation is given on the case, wherein the only onecontroller 10 comprises both the detecting means for detecting aparticular object (person) based on an external information signal S5fed from the external sensor 19 and the judging means for judgingwhether or not an object satisfies preset conditions (that the intensityof ‘Friendliness’ is greater than that of ‘Dislike’ by a given value)when an object is detected by the detecting means. However, the presentinvention is not limited to it, and these detecting and judging meansmay as well be installed on other separate units.

[0159] Furthermore, in the above mode of carrying out the presentinvention, elucidation is given on the case, wherein an actiongeneration control means for generating actions under control of theaction control mechanism 34 of the controller 10 comprises the actiondetermining mechanism 32 and the action generation mechanism 33 of thecontroller 10, a plurality of the actuators 21A˜21N, speaker 20, and theLEDs. However, the present invention is not limited to it, and isapplicable to a wide variety of other configurations.

[0160] Furthermore, in the above mode of carrying out the presentinvention, elucidation is given on the case, wherein the pet robot 1takes an action to approach an object if the intensity of ‘Friendliness’is greater than that of ‘Dislike’ by a given value, and otherwise itgoes away from the object. However, the present invention is not limitedto it, and it may as well be such that other actions such as ‘Bark’ and‘Turn on and off the light (blink)’ are achieved. It this case it may aswell be designed such that the greater the difference between theintensity of ‘Friendliness’ and that of ‘Dislike’, the more willfully apet robot behaves or the more quickly it goes away.

[0161] Furthermore, in the above mode of carrying out the presentinvention, elucidation is given on the case, wherein it is the conditionfor the pet robot 1 to approaches or goes away that the intensity of‘Friendliness’ is greater than that of ‘Dislike’ by a given value.However, the present invention is not limited to it, and a pet robot mayas well be so designed as to respond to a variety of other conditions,e.g., whether or not an object is registered.

[0162] Furthermore, in the above mode of carrying out the presentinvention, elucidation is given on the case, wherein a count value ofemotion in the emotion table 35 is altered when an action or a call isexerted. However, the present invention is not limited to it, and it mayas well be such that a count value in the emotion table 35 is alteredautomatically as time goes by, approaching the count value initiallyset. In this case a sense of friendliness or dislike the pet robot 1 hastoward a person with a particular face pattern is attenuated as timegoes by.

[0163] Furthermore, in the above mode of carrying out the presentinvention, elucidation is given on the case, wherein an action patternis determined based on a count value of the intensity of ‘Friendliness’and that of ‘Dislike’. However, the present invention is not limited toit, and it may as well be such that an action pattern is determinedbased on the count value of other emotions such as the intensity of‘Fear’, ‘surprise’, ‘Anger’, and ‘Grief’.

[0164] In this case, the action control mechanism 34 divides the countvalues in the emotion table 35 into one group of count values forapproaching an object like the intensity of ‘Friendliness’ and anothergroup of count values for going away from the object. The divided countvalues are coordinated, based on which an action pattern is determined.

[0165] Furthermore, in the above mode of carrying out the presentinvention, elucidation is given on the case, wherein such behaviors as‘Get angry’, ‘Rejoiced’, and ‘Run’ are used as condition transitionnodes. However, the present invention is not limited to it, and it mayas well be such that the transition probability is altered among thesenodes with the use of other emotions such as ‘Grieved’ and ‘Surprised’,and other actions such as ‘Cry’ and ‘Romp’.

[0166] Furthermore, in the above mode of carrying out the presentinvention, elucidation is given on the case, wherein the transitionprobabilities in action models are varied according to the intensity of‘Like or Dislike’ held toward a detected person. However, the presentinvention is not limited to it, and it may as well be designed such thata plurality of action models are prepared, which are interchangedaccording to the intensity of like or dislike; separate action modelsare prepared for a favorable person and an unfavorable person so thataction models for a favorable person, (which are so designed as todiscover an action expressing a sense of ‘Joy’ more easily), are usedfor a favorable person and that action models for an unfavorable person,(which are so designed as to discover an action expressing a sense of‘Dislike’ more easily), are used for an unfavorable person.

[0167] Industrial Applicability

[0168] The present invention can be applied to pet robots.

1. A robot comprising and characterized by: an external informationcapturing means for reading in information from the outside, a detectionmeans for detecting a particular object based on said informationcaptured by said external information capturing means, a judging meansfor judging whether or not the object satisfies given conditions whensaid object is detected by said detection means, an action generationmeans for generating a given action based on the results obtained bysaid judging unit,
 2. A robot as defined in the claim 1 characterizedby: said action generating means for generating said action ofapproaching toward said object detected by means of said objectdetecting means when said object satisfies said conditions.
 3. A robotas defined in the claim 2, wherein: said action generation meanscomprising, a condition recognition unit for recognizing said externalcondition based on said information captured by said externalinformation capturing unit, an emotion modeling unit for keeping aparameter value of the intensity of each emotion of at least ‘Joy’ and‘Dislike’ and for varying said parameter value of said emotions based onsaid judgment results at said condition recognition unit, an actiongeneration unit for generating said action based on said parameter valueindicating judgment results obtained at said condition recognition unitand the intensity of each of said emotion at said emotion modeling unit,an altering unit for altering setting of said emotion modeling unit, ifneed be, based on the judgment results at said judgment unit, and saidalteration means alters a setting of said emotion modeling means, incase said object satisfies said conditions so that it is easier toincrease said parameter value of said intensity of an emotion ‘joy’, andalters a setting of said emotion modeling means, in case said objectdoes not satisfy said conditions so that it is easier to increase saidparameter value of said intensity of an emotion ‘Dislike’.
 4. A robot asdefined in the claim 2, wherein: said generating means, comprising; acondition recognizing unit for recognizing said external conditionsbased on said information captured by said external informationcapturing unit, an action determining unit for determining saidsubsequent action with probability based on the recognition resultsobtained at said condition recognition unit, an action generation unitfor generating said action determined by said action determining unit,and, an alteration unit for altering said setting of the actiondetermining unit as required, based on said judgment results obtained bysaid judgment means, and said alteration unit for altering the settingof said action determining unit to increase the selection probabilityfor said action expressing ‘Joy’ if said object satisfies saidconditions, and for altering the setting of said action determining unitto increase the selection probability for said action expressing‘Dislike’ if said object does not satisfy said conditions.
 5. Controlmethod of a robot comprising: the first step for capturing externalinformation and detecting a particular object based on said information,the second step for making judgment on whether or not said objectsatisfies given conditions when said object is detected, and the thirdstep for making a robot which generates given actions based on saidjudgment results.
 6. Control means for a robot as defined in the claim 5wherein; said third step generates said action to approach said objectif said object satisfies said conditions, and generates said action togo away from said object if said object does not satisfies saidconditions.
 7. Control method of a robot as defined in the claim 6wherein: said robot comprising; a condition recognizing unit forrecognizing said external condition based on said information read in bysaid external information capturing unit, an emotion modeling unit forretaining the intensity of each emotion of at least ‘Joy’ and ‘Dislikes’as parameter value and for varying said parameter values of saidemotions based on recognition results at said condition recognitionunit, and an action generation unit for generating said actions based onsaid parameters indicating the recognition results at said conditionrecognition unit and the intensity of each of said emotions at saidemotion modeling unit, and said third step: alters the setting of saidemotion modeling unit to make it easier to increase said parameter valueindicating the intensity of said emotion ‘Joy’, if said object satisfiessaid conditions while, if said object does not satisfy said conditions,alters the setting of said emotion modeling unit to make it easier toincrease said parameter value indicating the intensity of said emotion‘Dislike’.
 8. Control method of a robot system as defined in the claim6, wherein: said robot comprising; a condition recognition unit forrecognizing said external condition based on said information capturedby means of said external information read-in means; an actiondetermining unit for deciding upon the subsequent action withprobability, based on the recognition results obtained at said conditionrecognition unit, and an action generation means for generating saidaction determined by said action determining unit, and the third step:alters the setting of said action determining to increase the selectionprobability of said action expressing ‘joy’ if said object satisfiessaid conditions, whereas, if said object does not satisfy saidconditions, alters the setting of said action determining means toincrease the selection probability expressing ‘Dislike’.